Digital Content Integration Using Neural Network Frame Scoring

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Solution Overview

Problem

Existing digital media editing techniques often adversely affect user experience by modifying content at specific time points in videos or audio recordings, such as inserting advertisements or informational messages, which can disrupt the flow of the content.

Innovation Solution

A computer-implemented method and system that identifies candidate host frames in digital content based on attributes like visual and audio data transitions, using neural networks and machine learning models to determine optimal insertion points for source content, minimizing disruption and enhancing user experience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If source digital content items are inserted into target digital content item, then productivity of content integration is improved, but user experience deteriorates due to disruption of content flow

Engineering Contradiction:

Improvecontent integration efficiencyVSAvoiduser experience disruption

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary analysis of the target digital content item to identify candidate host frames and determine candidate scores indicating transition degrees before actual insertion. This advance preparation allows selection of optimal insertion points that minimize disruption to user experience while maintaining efficient content integration.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual selection of insertion points with automated machine learning models and neural networks that analyze visual and audio attributes. This substitution enables objective, data-driven selection of host frames based on transition characteristics, eliminating subjective judgment and improving both efficiency and user experience.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Object-affected harmful factors

If manual selection of insertion points is used, then user experience is maintained, but productivity of content editing deteriorates

Engineering Contradiction:

Improveuser experience qualityVSAvoidcontent editing efficiency

Core Design Contradiction:

Object-affected harmful factorsVSProductivity

Solution Approach 1:

The system introduces an intermediary layer of automated analysis tools including visual attribute analyzers, audio attribute analyzers, and machine learning models that objectively identify optimal insertion points. This intermediary process maintains user experience quality by ensuring insertions occur at appropriate transitions while dramatically improving editing productivity through automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The target digital content item itself provides the information needed for optimal insertion through its own visual and audio attributes. The system extracts transition information directly from the content's inherent characteristics without requiring external guidance or manual input, enabling autonomous, efficient editing that preserves user experience.

Inventive Principle:

Principle #25Self-service

3Productivity

If insertion points are selected without analyzing content attributes, then productivity is improved, but manufacturing precision of integration quality deteriorates

Engineering Contradiction:

Improveintegration speedVSAvoidintegration quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system performs preliminary analysis of visual and audio attributes to identify candidate host frames and calculate candidate scores indicating transition degrees before actual insertion. This advance preparation ensures high integration quality by selecting frames with appropriate transition characteristics while maintaining productivity through automated processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent analyzes multiple parameters including visual attributes (color histograms, edge densities, motion vectors) and audio attributes (spectral features, temporal patterns) to determine optimal insertion points. By considering changes in these parameters, the system identifies frames where transitions naturally occur, ensuring high integration quality while maintaining efficient processing.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10522186B2Apparatus, systems, and methods for integrating digital media content

Publication Date: 2019.12.31 ADOBE INC

AI summary

Disclosed herein are techniques for digital content integration. A computer-implemented method includes receiving a target digital content item that includes a plurality of frames, identifying a set of candidate host frames for inserting source digital content items from the plurality of frames based on one or more attributes of the target digital content item, determining a candidate score for each respective candidate host frame of the candidate host frames, and generating host time defining data including identifications and the candidate scores of the candidate host frames, where the candidate score indicates a degree of transition of the target digital content item at the candidate host frame. One or more candidate host frames are then selected based on the candidate scores for inserting one or more source digital content items into the target digital content item.